Artificial Intelligence (AI) conversation programs, sometimes referred to as chatbots, provide casual conversations with humans. The chatbots can chime into the conversation with context-specific facts about things like celebrities, sports, or finance, while showing empathy, a sense of humor, and a healthy helping of sass. The chatbot can also tell jokes, read a horoscope, provide facts, and much more.
However, since chatbots draw their knowledge from many sources, including social networks, the information collected may result in negative or offensive language. To avoid misbehaving chatbots, mechanisms are put in place to track the performance of the chatbot and configure the chatbot to filter out offensive content.
Tracking the performance of the chatbot is difficult because there can be thousands or millions of users chatting with the chatbot, which creates a large amount of content to be examined. To monitor content, human labelers are used to flag potential problems. Also, machine learning algorithms and natural language processing techniques may be used for finding trouble that may appear during a conversation. In order to monitor user traffic, human labelers have to pay close attention to the logs that come in. This is especially important for blocking context in which a chatbot can say the wrong thing at the wrong time and bring down the brand that the chatbot represents. One problem with human monitoring of logs, however, is that it requires intense attention and focus and may become boring and monotonous, resulting in the possibility of failure.